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SVM Classification and Analysis of Margin Distance on Microarray Data

Shaik Abdul, Ameer Basha
http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924

Abstract Details

2011, Master of Science, University of Akron, Computer Science.
Support vector machine is statistical classification algorithm that classifies data by separating two classes with the help of a functional hyper plane. SVM is known for good performance on noisy and high dimensional data such as microarray. A marginal region of functional hyper plane named "danger zone‟ is defined to be the region between two parallel hyper planes that are determined by the average distances of the support vectors from the two classes to functional hyper plane. The main aim of this study was to determine the effect of margin distance, the width of the danger zone, on the accuracy of the classifier and to analyze the role of margin distance in feature selection. The study was carried out using three microarray datasets. For each dataset, equation of functional hyper plane separating the two classes of data was derived. The corresponding support vectors were obtained. The average distances between support vectors from the two classes to functional hyper plane were calculated. The relations between the width of the danger zone and the classification accuracy were investigated. The rate of change of the margin distance with respect to the number of features used for constructing the support vector machine was also examined. The results indicate that although correlation between margin and accuracy is not very strong, but the rate of change of classification accuracy with respect to margin distance can be employed to determine the optimal number of features for constructing high performance support vector machine for classifying microarray samples.
Zhong-Hui Duan, Dr. (Advisor)
Chien-Chung Chan, Dr. (Committee Member)
Yingcai Xiao, Dr. (Committee Member)
72 p.
Bioinformatics; Computer Science
SVM; Data mining; classification; microarray; support vectors; margin distance

Recommended Citations

Citations

  • Shaik Abdul, A. B. (2011). SVM Classification and Analysis of Margin Distance on Microarray Data [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924

    APA Style (7th edition)

  • Shaik Abdul, Ameer Basha. SVM Classification and Analysis of Margin Distance on Microarray Data. 2011. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.

    MLA Style (8th edition)

  • Shaik Abdul, Ameer Basha. "SVM Classification and Analysis of Margin Distance on Microarray Data." Master's thesis, University of Akron, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924

    Chicago Manual of Style (17th edition)

akron1302618924
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© 2011, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.